Predictive daylight harvesting system

ABSTRACT

A predictive system and method thereof for indoor horticulture are disclosed. The method includes obtaining a set of input values identifying a geographic position of a physical structure enclosing an interior environment and a target distribution for environmental parameters for a selected plant occupant. The method further includes obtaining a virtual representation of the physical structure, and iteratively over time, updating the virtual representation based on actual plant growth or a predicted plant growth model for the selected plant occupant. The method further includes running a computational model to obtain a predicted distribution of the environmental parameters for the virtual representation, and determining a target distribution of artificially modulated environmental parameters. Based on the target distribution of the artificially modulated environmental parameters, the method includes setting output parameters for control devices to collectively control the actual distribution of the set of environmental parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims priority to U.S. non-provisional application Ser. No. 14/792,590, titled PREDICTIVE DAYLIGHT HARVESTING SYSTEM, filed Jul. 6, 2015, which is a continuation-in-part application of and claims priority to U.S. non-provisional application Ser. No. 13/446,577, titled PREDICTIVE DAYLIGHT HARVESTING SYSTEM, filed Apr. 13, 2012, which is a non-provisional application of and claims priority to U.S. provisional application Ser. Nos. 61/457,509 and 61/565,195, titled PREDICTIVE DAYLIGHT HARVESTING METHOD AND APPARATUS, respectively filed Apr. 14, 2011 and Nov. 30, 2011, the entire contents of each of these applications are incorporated herein by reference in their entirety for all purposes. U.S. non-provisional application Ser. No. 14/792,590, titled PREDICTIVE DAYLIGHT HARVESTING SYSTEM, filed Jul. 6, 2015 is also a non-provisional application of and claims priority to U.S. provisional application Ser. No. 62/172,641, titled PREDICTIVE DAYLIGHT HARVESTING SYSTEM, filed Jun. 8, 2015, the entire contents of which are also incorporated herein by reference in their entirety for all purposes.

TECHNICAL FIELD

The subject matter of the present invention relates to the field of sustainable building lighting and energy control, and more particularly, is concerned with predictive daylight harvesting.

BACKGROUND

Electric lighting accounts for approximately 40 percent of all energy consumed in modern buildings. Incorporating available daylight can reduce these annual energy costs by 40 to 50 percent using “daylight harvesting” techniques. The basic principle of daylight harvesting is to monitor the amount of daylight entering an interior space and dim the electric lighting as required to maintain a comfortable luminous environment for the occupants. Where required, motorized blinds and electrochromic windows may also be employed to limit the amount of daylight entering the occupied spaces. Further energy savings can be realized through the use of occupancy sensors and personal lighting controls that operate in concert with the daylight harvesting system.

SUMMARY

Predictive daylight harvesting is disclosed in various forms. In an example, one or more input values are obtained that identify a target distribution of direct and interreflected radiation within an environment, a geographic location, a time of day, and a date. An expected sky condition is calculated for the geographic location, the time of day, and the date based on a mathematical model. The mathematical model incorporates and/or was previously trained on historical weather data. A predicted distribution of direct and interreflected solar radiation within the environment is calculated based on the expected sky condition. Measurement data from one or more photosensors is obtained that provides measurements of an initial distribution of direct and interreflected radiation within the environment, including radiation from solar and electrical lighting sources. A target distribution of direct and interreflected artificial electromagnetic radiation produced by electrical lighting is determined, based on the measurement data and the predicted distribution of direct and interreflected solar radiation, to achieve the target distribution of direct and interreflected radiation within the environment. Output parameters are set to one or more devices controlling admittance of solar radiation into the environment and/or controlling the electrical lighting to modify the initial distribution to achieve the target distribution of direct and interreflected radiation within the environment.

The disclosed and/or claimed subject matter is not limited by this summary as additional aspects are presented by the following written description and associated drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A shows a block diagram of the predictive daylight harvesting system for buildings.

FIG. 1B shows a block diagram of the predictive daylight harvesting system for greenhouses.

FIG. 2 shows an example of a finite element representation of a combined interior and exterior environment.

FIG. 3 shows the transfer of luminous flux from exterior environment elements through a window or opening to interior environment elements.

FIG. 4 shows a flowchart for operation of the predictive daylight harvesting system.

FIG. 5A shows a flowchart for reading the input data for buildings.

FIG. 5B shows a flowchart for reading the input data for greenhouses.

FIG. 6 shows a flowchart for calculating the optimal luminous and thermal environment.

FIG. 7A shows a flowchart for controlling the electric lighting, automated fenestration devices, and HVAC devices for buildings.

FIG. 7B shows a flowchart for controlling the electric lighting, automatic fenestration devices, HVAC devices, and supplemental carbon dioxide distribution devices for greenhouses.

FIG. 8 shows a schematic representation of a predictive daylighting harvesting system.

FIG. 9 shows a block diagram of a predictive daylight harvesting system.

FIG. 10 is a flow diagram depicting an example method.

FIG. 11 is a schematic diagram depicting an example computing system.

DETAILED DESCRIPTION

Electric lighting accounts for approximately 40 percent of all energy consumed in modern buildings. Incorporating available daylight can reduce these annual energy costs by 40 to 50 percent using “daylight harvesting” techniques. The basic principle of daylight harvesting is to monitor the amount of daylight entering an interior space and dim the electric lighting as required to maintain a comfortable luminous environment for the occupants. Where required, motorized blinds and electrochromic windows may also be employed to limit the amount of daylight entering the occupied spaces. Further energy savings can be realized through the use of occupancy sensors and personal lighting controls that operate in concert with the daylight harvesting system and are therefore considered integral thereto.

As a non-limiting example, the present invention provides a predictive daylight harvesting system for buildings which can be implemented as a method comprising the steps of: a) inputting data values regarding a plurality of variable building design parameters; b) calculating the effects on a building's environmental characteristics based on the data values regarding a plurality of building design parameters; c) changing at least one of the data values regarding variable building design parameters; d) recalculating the effects on a building's environmental characteristics based on the data values regarding a plurality of building design parameters. Buildings in the context of this patent are inclusive of any structure wherein the ambient environment may be augmented through controlled means, including but not limited to light, heat, and humidity. As a result buildings include, but are not limited to, residential premises, commercial premises (including offices and warehouses), aviculture facilities, agriculture facilities and animal husbandry facilities. In addition to buildings in general, specific reference will be made to greenhouses and aquaculture facilities without limiting the application of the predictive daylight harvesting system described herein.

Daylight harvesting is important wherever the amount of available daylight must be controlled or supplemented with electric lighting. Examples include hospitals and long-term care facilities where daily exposure to natural lighting is beneficial, fish farms and aquaponic facilities where overhead or underwater lighting is used to entrain the circadian rhythms of fish, and free-range aviculture where birds require a minimum amount of natural and electric lighting for optimum health and well-being.

The parameters may be used to control any building environmental system including lighting, heating, humidity or all of them together. This enables the selection of building design parameter settings to maximize energy savings while maintaining selected minimal occupant, including plant or animal, requirements, for example, for heating, lighting, and/or humidity. Daylight harvesting is well suited to lighting control and to a lesser extent heating control, but the system is not limited to that prime example of a use for the system.

Solar insolation is not necessarily a parameter to be used in the system, but it is a prime parameter to be considered in building design. In practice it would be preferred to include the steps of: a) measuring actual solar insolation and fine-tuning selected building design parameter settings to maximize energy savings while maintaining selected minimal occupant, including plant or animal, requirements for heating and lighting; b) analyzing a combination of historical weather data, in situ daylight measurements over time, and current weather predictions, and determining an optimal strategy for predictive daylight harvesting that maximizes energy savings while maintaining selected minimal occupant, including plant or animal, requirements for heating and lighting; c) analyzing occupant behavior, based on input from occupancy event sensors and personal lighting control actions, or plant or animal growth and health based on input from plant sensors and/or manual greenhouse operator input, and determining an optimal strategy for daylight harvesting that maximizes energy savings while maintaining selected minimal occupant, including plant or animal, requirements for heating and lighting, based on predicted occupant behavior, or plant or animal growth and health; and d) interacting with a building's HVAC control system and implementing an optimal strategy for maximizing energy savings while maintaining selected minimal occupant requirements, including plant or animal, for heating, ventilation, air-conditioning, and lighting.

The variable building design parameters would include one or more daylight photosensor locations, luminaire locations and illumination levels, occupancy sensor locations, temperature sensor locations and temperatures, humidity sensor locations and humidity levels, glazing transmittance characteristics, and thermal emissivity and thermal mass of objects and surfaces in a building's interior environment for the purpose of determining radiative heat transfer within the building environment due to solar insolation.

The variable greenhouse design parameters would include one or more daylight photosensor locations, luminaire locations and illumination levels, temperature sensor locations and temperatures, humidity sensor locations and humidity levels, soil moisture levels, carbon dioxide and oxygen gas concentrations, plant phytometrics, wind speed and direction, glazing transmittance characteristics, and thermal emissivity and thermal mass of objects and surfaces in a building's interior environment for the purpose of determining radiative heat transfer within the building environment due to solar insolation.

As another non-limiting example, in a basic implementation, the predictive daylight harvesting method would have the following steps: a) receiving input data from at least one ambient condition sensor and at least one information feed about anticipated solar conditions; b) calculating a luminous environment for a building based on the input data; c) generating messages based on output data about the calculated luminous environment; and d) transmitting the messages via an interconnect system to a building environmental control subsystem, in order to maximize energy savings while maintaining selected minimal occupant, including plant or animal, requirements for a building's environmental characteristics.

As yet another non-limiting example, the predictive daylight harvesting system can be implemented as an apparatus having a controller that: a) reads input data from a variety of sensors and information feeds, the sensors and feeds to include at least a plurality of sensors and information feeds from among the class of sensors and information feeds that includes daylight photosensors, temperature sensors, occupancy sensors, humidity sensors, soil moisture sensors, gas concentration sensors, anemometers, timers, personal lighting controls, utility smart power meters, weather report feeds, HVAC and energy storage controllers; b) calculates the effects of variable building design parameters on building environment characteristics, such as light, heat, and humidity balance and on energy management; and c) outputs building design parameter setting command signals, in order to maximize energy savings while maintaining selected minimal occupant, including plant or animal, requirements for the building environment characteristics respectively. The controller would read input data from a variety of sensors and information feeds, including but not limited to daylight photosensors, temperature sensors, occupancy sensors, humidity sensors, soil moisture sensors, gas concentration sensors, anemometers, timers, personal lighting controls, utility power meters, weather report feeds, and other energy management systems, including HVAC and energy storage controllers. The controller would receive and process information about light fixtures and light sources (luminaires) located in a building's interior environment, including photometric and electrical properties of the luminaires.

The building control system would be enhanced by having the controller maintain and access virtual representations of a building's exterior and interior environments, including the geometry and material properties of objects that may significantly influence the distribution of daylight and artificial (for example, electrically-generated) luminous flux within the environments, such as luminaires located in the interior environment, including their photometric and electrical properties, daylight photosensors located in the interior and optionally exterior environments, and occupancy sensors located in the interior environment. These virtual representations of building exterior and interior environments would be accessed by the controller to perform calculations on the effects of solar insolation on building heat balance and energy management, using for example known methods as described in BS EN 15242:2007, Ventilation for buildings—Calculation methods for the determination of air flow rates in buildings including infiltration. Specifically, virtual representations of thermal emissivity and heat capacity (thermal mass) of objects and surfaces in a building's interior environment, would accessed by the controller for the purpose of determining radiative and conductive heat transfer within the environment due to solar insolation. Additionally, virtual representations of occupants and their behaviors, including where the occupants are likely to be located within a building's interior environments at any given time and date, and the occupants' personal lighting preferences, would be accessed by the controller for the purpose of calculating optimal output settings for building design parameter setting command signals in order to maximize energy savings while maintaining selected minimal occupant requirements for heating and lighting.

Similarly, a greenhouse control system would be enhanced by having the controller maintain and access virtual representations of a greenhouse's exterior and interior environments, including the geometry and material properties of objects that may significantly influence the distribution of daylight and artificial (for example, electrically-generated) luminous flux within the environments, such as luminaires located in the interior environment, including their photometric and electrical properties, daylight photosensors, and humidity sensors located in the interior and optionally exterior environments, soil moisture and gas concentration sensors located in the interior environment, and wind speed and direction sensors located in the exterior environment. These virtual representations of building exterior and interior environments would be accessed by the controller to perform calculations on the effects of solar insolation on greenhouse heat balance and energy management, using for example known methods as described in BS EN 15242:2007, Ventilation for buildings—Calculation methods for the determination of air flow rates in buildings including infiltration. Specifically, virtual representations of thermal emissivity and heat capacity (thermal mass) of objects and surfaces in a greenhouse's interior environment, would accessed by the controller for the purpose of determining radiative and conductive heat transfer within the environment due to solar insolation. Additionally, virtual representations of plant growth and health, including the plants' photosynthesis process, temperature, humidity and other environmental requirements, would be accessed by the controller for the purpose of calculating optimal output settings for greenhouse design parameter setting command signals in order to maximize energy savings while maintaining selected minimal plant requirements for heating and lighting.

Optimally, the building control system would include a fuzzy-logic inference engine that learns weather patterns and occupant usage patterns and preferences, and the controller would maintain a mathematical model of sky conditions, historical weather data, and a database of weather patterns and occupant usage patterns and preferences, continually reads data from external input and communication devices, calculates the optimal settings for luminaires and fenestration devices, and controls luminaires and fenestration devices to achieve maximal energy savings while providing an interior luminous and thermal environment that is consistent with predefined goals and occupant preferences.

Similarly, an optimal greenhouse control system would include a fuzzy-logic inference engine that learns weather patterns and optimal plant and animal growth and health parameters, and the controller would maintain a mathematical model of sky conditions, historical weather data, and a database of weather patterns and plant short-term and long-term environmental requirements, continually reads data from external input and communication devices, calculates the optimal settings for luminaires and fenestration devices, and controls luminaires, fenestration devices, humidity control devices, and supplemental carbon dioxide distribution devices to achieve maximal energy savings while providing an interior luminous and thermal environment that is consistent with predefined goals and plant requirements.

In one elementary form, the predictive daylight harvesting system would also comprise: a) at least one controller that reads input data from a variety of sensors and information feeds, and that includes an artificial intelligence engine; b) at least one ambient condition sensor and at least one information feed; and c) an interconnect system operatively coupling the controller to the sensor and the information feed, configured to provide output data suitable for dimming or switching luminaires and operating automated fenestration and other environmental devices.

The controller may further maintain communication with other building automation subsystems, including but not limited to HVAC and energy storage systems. It may also maintain communication with external systems such as electrical power utilities and smart power grids.

In a preferred mode of operation, the controller would continually read data from the external input and communication devices, calculate the optimal settings for the luminaires, fenestration, and other environmental control devices, and control those devices to achieve maximal annual energy savings while providing an interior luminous and thermal environment that is consistent with predefined goals and occupant preferences or plant or animal requirements. The “what-if” scenarios capability of the invention deriving from its simulating a virtual building interior environment on a regular basis (for example, hourly) enable a physical daylight harvesting controller system to be designed (for example, including an optimal layout of daylight photosensors) and programmed accordingly. The controller can then further access the virtual representation during operation to refine its behavior in response to the building performance by means of “what-if” simulations.

The present invention is herein described more fully with reference to the accompanying drawings, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms, and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. As used in the present disclosure, the term “diffuse horizontal irradiance” refers to that part of solar radiation which reaches the Earth as a result of being scattered by the air molecules, aerosol particles, cloud particles, or other airborne particles, and which is incident upon an unobstructed horizontal surface, the term “direct normal irradiance” refers to solar radiation received directly from the Sun on a plane perpendicular to the solar rays at the Earth's surface, the term “solar insolation” refers to the solar radiation on a given surface, and the terms “illuminance,” “irradiance,” “luminous exitance,” “luminous flux,” “luminous intensity,” “luminance,” “spectral radiant flux,” “spectral radiant exitance,” and “spectral radiance” are commonly known to those skilled in the art and as defined in ANSI/IES RP-16-10, Nomenclature and Definitions for Illuminating Engineering.

As used in the present disclosure, the term “photosynthetically active radiation” (abbreviated “PAR”) refers to the spectral range of electromagnetic radiation (either solar or electrically-generated) from 400 nm to 700 nm that photosynthetic organisms are able to use in the process of photosynthesis. It is commonly expressed in units of micromoles per second (μmol/sec).

As used in the present disclosure, the term “photosynthetic photon flux density” (abbreviated “PPFD”) refers to the density of photosynthetic photons incident upon a physical or imaginary surface. It is commonly measured in units of micromoles per square meter per second (μmol/m2-sec) with a spectrally-weighted “quantum” photosensor.

As used in the present disclosure, the term “ultraviolet radiation” refers to the spectral range of electromagnetic radiation (either solar or electrically-generated) from 280 nm to 400 nm. It may be further referred to as “ultraviolet A” (abbreviated “UV-A”) with spectral range of 280 nm to 315 nm, and “ultraviolet B” (abbreviated “UV-B”) with a spectral range of 315 nm to 400 nm, in accordance with ISO Standard 21348:2007, “Space environment (natural and artificial)—Process for determining solar irradiances.” It may be expressed in units of micromoles per second (μmol/sec) or watts.

As used in the present disclosure, the term “visible light” refers to the spectral range of electromagnetic radiation (either solar or electrically-generated) from 400 nm to 700 nm.

As used in the present disclosure, the term “infrared radiation” refers to the spectral range of electromagnetic radiation (either solar or electrically-generated) from 700 nm to 850 nm for horticultural purposes, and from 700 nm to 2800 nm for solar insolation purposes. It may be expressed in units of micromoles per second (μmol/sec) or watts.

As used in the present disclosure, the term “environment” may refer to a virtual environment comprised of a finite element model or similar computer representation with virtual sensors and control devices, or a physical environment with physical sensors and control devices.

As used in the present disclosure, the term “luminous environment” refers to a physical or virtual environment wherein visible light, ultraviolet radiation, and/or infrared radiation, is distributed across material surfaces.

As used in the present disclosure, the term “solar heat gain” refers to the increase in temperature in an object that results from solar radiation.

Electric lighting accounts for approximately 40 percent of all energy consumed in modern buildings. It has long been recognized that incorporating available daylight can reduce these annual energy costs by 40 to 50 percent using “daylight harvesting” techniques.

Daylight harvesting is also an essential feature of agricultural greenhouses, whose primary function is to provide a controlled environment for growing vegetables or flowers. Optimizing daylight usage minimizes supplemental lighting and heating costs while maintaining optimal growing conditions.

The basic principle of daylight harvesting is to monitor the amount of daylight entering an interior space and dim the electric lighting as required to maintain a comfortable luminous environment for the occupants. Where required, motorized blinds and electrochromic windows may also be employed to limit the amount of daylight entering the occupied spaces.

The same principle applies to greenhouses, where it is necessary to provide supplemental electric lighting and heating or ventilation as required to maintain optimal growing conditions. Where required, motorized shading devices and electrochromic windows may also be employed to limit the amount of daylight entering the greenhouse spaces.

Further energy savings can be realized through the use of occupancy sensors and personal lighting controls that operate in concert with the daylight harvesting system and are therefore considered integral thereto. At the same time, the building occupants' comfort and productivity must be taken into consideration by for example limiting visual glare due to direct sunlight.

Yet further energy savings can be realized through the use of transparent or semi-transparent solar panels, particularly those which absorb and convert to electricity ultraviolet and/or infrared radiation while remaining substantially transparent to visible light. The performance of such panels when used for building glazing thereby becomes integral to the performance of the daylight harvesting system.

Daylight harvesting is also related to solar insolation management in that the infrared solar irradiation entering interior spaces is absorbed by the floors, walls and furniture as thermal energy. This influences the building heat and humidity balance that must be considered in the design and operation of heating, ventilation and air conditioning (HVAC) systems. For example, the energy savings provided by electric light dimming may need to be balanced against the increased costs of HVAC system operation needed to maintain building occupant's comfort and productivity or plant growth and health. The use of temperature sensors and humidity sensors are therefore also considered integral to a daylight harvesting system.

For greenhouses, the amount of daylight entering the greenhouse space is directly related to plant photosynthesis and water evaporation. Further energy savings and optimization of plant growth and health can therefore be realized through the use of temperature sensors, humidity sensors, soil moisture sensors, carbon dioxide and oxygen concentration sensors, and sensors for directly monitoring plant growth and health (for example, U.S. Pat. No. 7,660,698 and U.S. Pat. No. 8,836,504). Such sensors operate in concert with the daylight harvesting system and are therefore considered integral thereto.

Related to solar insolation management is the design and operation of solar energy storage systems for high performance “green” buildings, wherein thermal energy is accumulated by means of solar collectors and stored in insulated tanks or geothermal systems for later use in space heating. Similarly, electrical energy may be accumulated by means of solar photovoltaic panels or wind power plants and stored in batteries for later use in operating electric lighting. The decision of whether to store the thermal and/or electrical energy or use it immediately is dependent in part on daylight harvesting through the operation of motorized blinds and other active shading devices that affect solar insolation of interior surfaces.

The operation of such systems may therefore benefit from weather data and other information that is shared by the daylight harvesting system. More generally, the design and operation of a daylight harvesting system is advantageously considered a component of an overall building automation system that is responsible for energy usage and conservation.

Annual lighting energy savings of 40 to 50 percent are possible with well-designed electric lighting systems incorporating daylight harvesting, even as standalone controllers that function independently of the HVAC systems. However, it has also been shown that roughly half of all installed daylight harvesting systems do not work as designed. These systems do not provide significant energy savings, and in many cases have been physically disabled by the building owners due to unsatisfactory performance.

The underlying problem is that the performance of these systems is determined by many parameters, including lamp dimming (continuous versus bi-level switched), photosensor placement and orientation, photosensor spatial and spectral sensitivity, luminaire zones, timer schedules, occupancy sensors, interior finishes, window transmittances, exterior and interior light shelves, motorized blinds and other shading devices, and personal lighting controls. In addition, the presence of exterior occluding objects such as other buildings, large trees and surrounding geography need to be considered, as do the hour-by-hour weather conditions for the building site.

A similar underlying problem exists with greenhouses in that the performance of greenhouse control systems is determined by many parameters, including lamp switching, photo sensor placement and orientation, photosensor spatial and spectral sensitivity, luminaire zones, timer schedules, humidity sensors, soil moisture sensors, gas sensors, and plant monitoring sensors, interior finishes, exterior and interior light shelves and similar reflectors, motorized shading devices, heating and ventilation devices, humidity control devices, supplemental carbon dioxide distribution devices, and greenhouse operator controls. In addition, the presence of exterior occluding objects such as other buildings, large trees and surrounding geography need to be considered, as do the hour-by-hour weather conditions for the greenhouse building site. Wind direction and speed is also a concern, due to the typically high rate of conductive heat loss through the greenhouse glazing system.

Given all this, there are at present no suitable design tools for the architect or lighting designer to simulate the performance of such complex systems. In particular, the current state-of-the-art in lighting design and analysis software requires ten of minutes to hours of computer time to simulate a single office space for a given time, date, and geographic location. Architects and lighting designers have no choice but to calculate the lighting conditions at noon on the winter and summer solstices and spring and fall equinoxes, then attempt to estimate the performance of their daylight harvesting designs for the entire year based solely on this limited and sparse information.

Existing daylight harvesting systems are therefore designed according to basic rules of thumb, such as “locate the photosensor away from direct sunlight” and “dim the two rows of luminaires closest to the south-facing windows.” There is no means or opportunity to quantify how the system will perform when installed.

There are software tools for building HVAC systems design that take solar insolation into account, but they do so in the absence of design information concerning daylight harvesting and electric lighting. The mechanical engineer typically has to assume so many watts per unit area of electric lighting and design to worst-case conditions, with no consideration for energy savings due to daylight harvesting.

Once the daylight harvesting system has been installed, it must be commissioned. This typically involves a technician visiting the building site once on a clear day and once at night to calibrate the photosensor responses. This is mostly to ensure that the system is working, as there is no means or opportunity to adjust the feedback loop parameters between the photosensors and the dimmable luminaires for optimum performance. Indeed, many existing daylight harvesting systems operate in open loop mode without any feedback, mostly for the sake of design simplicity.

There is therefore a need for a method and apparatus to accomplish five related tasks: First, there is a need for a method whereby an architect or lighting designer can fully and interactively simulate the performance of a daylight harvesting design. That is, the method should account for multiple system parameters that may influence the system performance, including the effects of solar insolation on building heat balance and energy management. The method should simulate the overall system such that an hour-by-hour simulation for an entire year can be generated in at most a few minutes. Given this interactive approach, an architect or lighting designer can experiment with many different designs or variations on a design in order to determine which design maximizes annual building energy savings while respecting building occupants' comfort or plant or animal growth and health requirements.

Second, there is a need for an apparatus that uses the aforesaid method to physically implement the simulated system, and which has the ability to self-tune its initial parameters (as determined by the simulation) in order to maximize annual energy savings.

Third, there is a need for an apparatus that can analyze a combination of historical weather data, in situ daylight measurements over time, current weather predictions, and other information using the aforesaid method to autonomously determine an optimal strategy for predictive daylight harvesting that maximizes annual energy savings.

Fourth, there is a need for an apparatus that can analyze occupant behavior, including occupancy sensor events and personal lighting controls actions such as luminaire dimming and switching and setting of personal time schedules, in buildings to autonomously determine an optimal strategy for daylight harvesting that maximizes annual energy savings based on predicted occupant behavior.

Similarly, there is a need for an apparatus that can analyze plant growth and health as determined by automatic plant growth sensors or manual greenhouse operator input, and autonomously determine an optimal strategy for daylight harvesting that maximizes annual energy savings based on predicted plant growth and health.

Fifth, there is a need for a daylight harvesting apparatus that can coordinate its operation with HVAC systems, solar energy storage systems, and other high performance “green” building management technologies that are influenced by solar insolation for the purposes of maximizing annual energy savings and building occupants' comfort and productivity or plant growth and health.

FIG. 1A shows an apparatus for enabling a predictive daylight harvesting system for a building. As shown, it is logically, but not necessarily physically, comprised of three components: 1) inputs 10 to 40; 2) controller 45, user interface 50 and communications port 55; and 3) outputs 60 and 65.

FIG. 1B shows an apparatus for enabling a predictive daylight harvesting system for a greenhouse. As shown, it is logically, but not necessarily physically, comprised of three components: 1) inputs 100 to 140; 2) controller 145, user interface 150 and communications port 155; and 3) outputs 160 to 175.

Inputs

Referring to FIG. 1A, the inputs include daylight photosensors 10, occupancy or vacancy sensors 15, timers 20, personal lighting controls 25, utility power meters 30, weather report feeds 35, and optional temperature sensors 40.

Referring to FIG. 1B, the inputs include daylight photosensors 100, temperature sensors 105, timers 110, humidity sensors 115, and optional wind speed and direction sensors 120, soil moisture sensors 125, carbon dioxide and/or oxygen sensors 130, utility smart power meters 135, and weather report feeds 140.

For the purposes of this application, a photosensor 10 or 100 includes any electronic device that detects the presence of visible light, infrared radiation (IR), and/or ultraviolet (UV) radiation, including but not limited to photoconductive cells and phototransistors. The photosensor may be spectrally weighted to measure units of illuminance, irradiance, PPFD, ultraviolet power flux density, infrared power flux density, or spectral radiant flux.

For the purposes of this application, a gas concentration sensor 130 includes any electronic device that measures the partial pressure of a gas, including but not limited to, carbon dioxide and oxygen. An example of a carbon dioxide gas concentration sensor is disclosed in Willa et al. 2014. “When Nanoparticles Meet Poly(ionic Liquid)s: Chemoresistive CO2 Sensing at Room Temperature,” Advanced Functional Materials 25(17):2537-2542.

Spectral radiant flux may be measured with a spectroradiometer. Such instruments are useful for greenhouse application in that while knowledge of photosynthetically active radiation (PAR) and PPFD is useful for predicting plant growth and health on a per-species basis, it only measures visible light integrated across the spectral range of 400 nm to 700 nm. It is known however that ultraviolet radiation may induce changes in leaf and plant morphology through photomorphogenesis (for example, Zuk-Golaszewska et al. 2003, “The Effect of UV-B Radiation on Plant Growth and Development,” Plant Soil Environ 49(3):135-140), that infrared radiation influences seed germination, flower induction, and leaf expansion (for example, Smith 2000, “Phytochromes and Light Perception by Plants—An Emerging Synthesis”, Nature 407:585-591), and that the presence of green light is important to many plants (for example, Folta et al. 2007, “Green Light: A Signal to Slow Down or Stop,” Journal of Experimental Botany 58(12):3099-3111). The response of different plant species to spectral power distributions becomes important as multi-color LED luminaires replace high-pressure sodium and other lamp types with fixed spectral power distributions.

Similarly for the purposes of this application, an occupancy or vacancy sensor 15 includes any electronic device that is capable of detecting movement of people, including but not limited to pyroelectric infrared sensors, ultrasonic sensors, video cameras, RFID tags, and security access cards.

Commercial daylight photosensors 10 or 100 may feature user-selectable sensitivity ranges that may be specified in foot-candles or lux (illuminance), watts/m2 (irradiance), or μmol/m2-sec (PPFD). Additionally the photosensor may include a field of view which may be specified in degrees. For the purposes of the present invention the spatial distribution of the photosensor sensitivity within its field of view, the spectral responsivity of the photosensor in the visible, infrared and ultraviolet portion of the electromagnetic spectrum, and the transfer function of the electrical output from the photosensor are germane. These device characteristics enable the present invention to more accurately simulate their performance, though the exclusion of one or more of these parameters will not prevent the system from functioning.

Daylight photosensors may be positioned within an interior environment (such as the ceiling of an open office) such that they measure the average luminance of objects (such as floors and desktops) within their field of view. Positioning of the photosensors may be to locate the photosensor away from direct sunlight. However, so-called “dual-loop” photosensors as disclosed in U.S. Pat. No. 7,781,713 and U.S. Pat. No. 7,683,301 may be positioned in for example skywells and skylights to measure both the average luminance of objects below and the direct sunlight and diffuse daylight incident upon the photosensors from above.

An alternative is to locate a daylight photosensor in each luminaire. In this example, the luminaire may be dimmed according to how much ambient light is detected by its photosensor.

Commercial occupancy and vacancy sensors 15 may employ pyroelectric, ultrasonic, or optical detection technologies, or a combination thereof. It can be difficult to accurately characterize the performance of these devices in enclosed spaces, as they may be influenced by the thermal emissivity of building materials for reflected far-infrared radiation and the acoustic reflectance of building materials for reflected ultrasonic radiation. In the present invention, occupancy sensors work best according to line-of-sight operation within their environments and in accordance with their sensitivity in terms of detection distance. Other motion detection techniques may also be employed.

Timers 20 or 110 may be implemented as external devices that are electrically or wirelessly connected to the building controller 45 or greenhouse controller 145, or they may be implemented within the hardware and software of said controller and accessed through the building controller's user interface 50 or greenhouse controller's user interface 150.

Personal lighting controls 25 may be implemented as for example handheld infrared or wireless remote controls or software programs executed on a desktop computer, a laptop computer, a smartphone, a personal digital assistant, or other computing device with a user interface that can be electrically or wirelessly connected to the building controller 45, including an Internet connection, on a permanent or temporary basis. The controls optionally enable the occupant or user to specify for example preferred illuminance levels in a specific area of the interior environment (such as for example a open-office cubicle or private office), to control selected motorized blinds or electrochromic windows, to influence (such as by example voting) the illuminance levels in a shared or common area of the interior environment, to specify minimum and maximum preferred illuminance levels, to specify the time rate of illumination level increase and decrease (“ramp rate” and “fade rate”), to specify time delays for occupancy sensor responses, and to specify individual time schedules.

Utility smart power meters 30 or 135 can provide real-time information on power consumption by buildings, in addition to information on variable power consumption rates that may change depending on the utility system load and policies. Such meters may be electrically or wirelessly connected to the building controller 45 or greenhouse controller 145, including an Internet connection.

Real-time weather report feeds 35 or 140 are widely available including on the Internet. These feeds can be connected to the building controller 45 or greenhouse controller 145 via a suitable electrical or wireless connection.

Geographic Information Systems (GIS) data available through various sources can additionally provide relevant environmental data, again using a feed connected to the building controller 45 or greenhouse controller 145 via a suitable electrical or wireless connection.

Temperature sensors 45 or 105 may be employed to measure room or space temperatures if such information is not available from an external HVAC controller or building automation system (not shown) in communication with building controller 45 or greenhouse controller 145.

Controller

In an embodiment, the building controller 45 or greenhouse controller 145 is a standalone hardware device, advantageously manufactured to the standards of standalone industrial computers to ensure continuous and reliable operation. It can however be implemented as a module of a larger building automation system.

In another embodiment, the building controller 45 or greenhouse controller 145 is comprised of a standalone hardware device, such as for example a communications hub, that is in communication with an external computing device, such as for example a centralized server, a networked remote computer system, or a cloud-based software-as-a-service.

The building controller may further comprise a user interface 50 and one or more communication ports 55 for communication with operators and external systems, including HVAC controllers, energy storage system controllers, building automation systems, and geographically remote devices and systems (not shown). Similarly, the greenhouse controller 145 may further comprise a user interface 150 and one or more communication ports 155 for communication with operators and external systems, including HVAC controllers, energy storage system controllers, building automation systems, and geographically remote devices and systems (not shown).

The operation of the building controller 45 or greenhouse controller 145 is as disclosed below following a description of the outputs.

Outputs

Referring to FIG. 1A, building controller 45 provides electrical signals to the dimmable or switchable luminaires 60 and optionally automated fenestration devices 65, such as for example motorized window blinds and electrochromic windows whose transmittance can be electrically controlled. Said electrical signals may include analog signals, such as for example the industry-standard 0-10 volt DC or 4-20 milliamp signals, and digital signals using a variety of proprietary and industry-standard protocols, such as DMX512 and DALI. The connections may be hard-wired using for example an RS-485 or derivative connection, an Ethernet connection, or a wireless connection, such as for example Bluetooth, Zigbee, 6LoWPAN, or EnOcean.

Referring to FIG. 1B, greenhouse controller 145 provides electrical signals to the dimmable or switchable luminaires 160, and optionally automated fenestration devices 165, such as for example motorized window blinds and electrochromic windows whose transmittance can be electrically controlled, in addition to optional humidity control devices 170 and carbon dioxide distribution devices 175. Said electrical signals may include analog signals, such as for example the industry-standard 0-10 volt DC or 4-20 milliamp signals, and digital signals using a variety of proprietary and industry-standard protocols, such as DMX512 and DALI. The connections may be hard-wired using for example an RS-485 or derivative connection, an Ethernet connection, or a wireless connection, such as for example Bluetooth, Zigbee, 6LoWPAN, or EnOcean.

Controller Operation

In one embodiment, the building controller 45 or greenhouse controller 145 maintains or accesses a three-dimensional finite element representation of the exterior and interior environments, such as is shown for example in FIG. 2. The representation is comprised of a set of geometric surface elements with assigned surface properties such as reflectance, transmittance, and color, and one or more electric light sources (“luminaires”) with assigned photometric data, such as luminous intensity distribution. For the purposes of solar insolation analysis, thermal emissivity and heat capacity properties may also be included.

Radiative flux transfer or ray tracing techniques can be used to predict the distribution of luminous flux or spectral radiant flux emitted by the luminaires within the interior environment due to interreflections between surface elements, as disclosed in for example U.S. Pat. No. 4,928,250. An example of a commercial lighting design and analysis software products that employs such techniques is AGi32 as manufactured by Lighting Analysts Inc. (Littleton, Colo.). As disclosed by U.S. Pat. No. 4,928,250, said techniques apply to both visible and invisible radiation, including the distribution of infrared radiation due to solar insolation.

For daylighting analysis, the representation of the exterior environment may include other buildings and topographical features that may occlude direct sunlight and diffuse daylight from entering the windows and openings of the interior environment. Similarly, said buildings and topographical features may reflect direct sunlight and diffuse daylight into the interior environment via the windows and openings.

In a rural setting, for example, the site topography may be extracted from a geographic information system and represented as a set of finite elements. In an urban setting, the geometry and material properties of nearby buildings may be extracted from a series of photographs, such as are available for example from Google Street View.

Exterior Environments

For exterior environments, the light sources are direct solar irradiance and diffuse irradiance from the sky dome. Given the direct normal and diffuse horizontal irradiance measurements for a given time of day and Julian date, and the geographical position of the site in terms of longitude and latitude, the Perez Sky or similar mathematical model may be used to accurately estimate the distribution of sky luminance for any altitude and azimuth angles. The irradiance measurements may be made in situ or derived from historical weather data such as from the Typical Meteorological Year (TMY) database for the nearest geographic location. (Infrared direct normal and diffuse horizontal irradiance measurements are also often available.)

The Perez sky model does not include spectral power distributions for daylight. However, a validated extension to the Perez sky model (Hosek and Wilkie 2012, “An Analytic Model for Full Spectral Sky-Dome Radiance,” ACM Transactions on Graphics 31(4) Article 5) can be used to predict daylight spectral power distributions that includes the ability to model ultraviolet radiation.

Typical Meteorological Year (TMY3) database records also provide hourly readings for cloud coverage, dry-bulb temperature, dew-point temperature, relative humidity, wind direction, and wind speed, and other information that can be incorporated into the simulation modeling for greenhouse controllers. A full listing is provided in Wilcox and Marion 2008, Users Manual for TMY3 Data Sets, National Renewable Energy Laboratory Technical Report NREL/TP-581-43156.

Using radiative flux transfer techniques, the distribution of luminous or spectral radiant flux due to direct sunlight and diffuse daylight within the exterior environment can be predicted. In an embodiment, the sky dome is divided into a multiplicity of horizontal bands with equal altitudinal increments. Each band is then divided into rectangular “sky patches” at regular azimuthal increments such that each patch has roughly equal area. An example of such a division that yields 145 patches is commonly referred to as the Tregenza sky dome division. A preferred division however may be obtained by recursive subdivision of an octahedron to yield 256 approximately equal-area patches.

In the same embodiment, the daily solar path across the sky dome throughout the year is similarly divided into a multiplicity of bands that parallel the solar path for selected dates, such as for example December 21, February 1/November 9, February 21/October 21, March 9/October 5, March 21/September 21, April 6/September 7, April 21/August 21, May 11/August 4, and June 21. The multiplicity of bands will span 47 degrees, regardless of the site latitude. Each band is then divided into rectangular “solar patches” at regular time intervals (for example, one hour) such that each patch has roughly equal area. A subdivision with for example nine bands and one-hour intervals will be comprised of 120 solar patches.

In one embodiment, spectral radiant flux is represented as three or more separate color bands that are identified for example as “red,” “green,” and “blue” in correspondence with their perceived colors, and is commonly practiced in the field of computer graphics. In another embodiment, more color bands may be employed. For example, the spectral responsivity of daylight photosensors may extend from ultraviolet to infrared wavelengths. It may therefore be advantageous to represent spectral radiant flux as for example 50 nanometer-wide color bands from 300 nm to 1200 nm. In yet another embodiment, a single radiant flux value may suffice, such as for example to represent infrared radiation for solar insolation analysis.

For greater accuracy in representing the sky dome luminance distribution, a sky dome division may alternately be chosen such that the differences between adjacent patch luminances are minimized. For example, the sun will traverse a 47-degree wide band of the sky dome over a period of one year. Smaller patches within this band may be employed to more accurately represent the sky dome luminance for any given time and Julian date.

According to prior art as implemented for example by AGi32, each sky dome patch represents an infinite-distance light source that illuminates the exterior environment with a parallel light beam whose altitudinal and azimuthal angles are determined by the patch center, and whose luminous intensity is determined by the Perez sky model. (Other sky models, such as the IES and CIE Standard General Sky, may also be employed to predict the sky dome's spatial luminance distribution.) Similarly, each solar patch represents an infinite-distance light source that illuminates the exterior environment with a parallel light beam whose altitudinal and azimuthal angles are determined by the patch center, and whose luminous intensity is determined by the Perez sky model. Once the luminous flux contributions from the direct sunlight and diffuse daylight have been calculated and summed, radiative flux transfer techniques such as those disclosed in U.S. Pat. No. 4,928,250 can be employed to calculate the distribution of luminous or spectral radiant flux due to interreflections between surface elements in the exterior environment.

For greenhouse applications, it is also possible to model crop canopies for use with radiative flux transfer techniques such as those disclosed in U.S. Pat. No. 4,928,250, for example Chelle and Andrieu 2007, “Modelling the Light Environment of Virtual Crop Canopies,” Functional-Structural Plant Modelling in Crop Production, Vos et al. Eds, pp. 75-89. Such models can be updated as the greenhouse crops grow and mature.

As will be known to those skilled in the arts of thermal engineering or computer graphics, each surface element in the finite element representation is assigned a parameter representing the luminous or spectral radiant flux that it has received but not yet reflected and/or transmitted (the “unsent flux”), and another parameter representing its luminous or spectral radiant exitance. (Infrared and ultraviolet radiant flux and exitance may also be considered without loss of generality.) At each iteration of the radiative flux transfer process, the unsent flux from a selected element is transferred to all other elements visible to that element. Depending on the reflectance and transmittance properties of each element, some of the flux it receives is reflected and/or transmitted; the remainder is absorbed. The flux that is not absorbed is added to both its unsent flux and luminous exitance parameters. The total amount of unsent flux thus decreases at each iteration, and so the radiative flux transfer process converges to a “radiosity” solution, wherein the luminous exitance or spectral radiant exitance of every surface element is known.

Canonical Radiosity

In a novel contribution of the present invention, this approach is extended by assigning a multiplicity of ‘n’ unsent flux parameters and ‘n’ luminous or spectral radiant exitance parameters to each exterior environment element, where ‘n’ is the total number of sky and solar patches. Each sky patch and solar patch ‘i’ is assigned unit luminance or spectral radiance, and its contribution to the luminous or spectral radiant flux of each exterior element is saved in its ‘i’th unsent flux and luminous or spectral radiant exitance parameters.

Once the luminous or spectral radiant flux contributions from the diffuse daylight (but not direct sunlight) have been calculated, radiative flux transfer techniques can be employed to calculate the distribution of luminous or spectral radiant flux due to interreflections between surface elements in the exterior environment for each sky and solar patch. The result is the generation of ‘n’ separate radiosity solutions for the exterior environment. Because the sky and solar patch luminances were not considered, these are referred to as ‘canonical’ radiosity solutions.

A particular advantage of this approach is that approximately 95 percent of the computational time needed to calculate the distribution of luminous flux due to interreflections between surface elements is devoted to calculating the “form factors” (i.e., geometric visibility) between elements, as disclosed in U.S. Pat. No. 4,928,250. Thus, if the prior art approach requires ‘x’ amount of time to calculate a single radiosity solution, the present approach requires ‘x’+‘y’ time, where ‘y’ is typically less than five percent. (The remainder of the computational time is consumed by other “housekeeping” activities related to manipulating the geometric and material data.)

Once the ‘n’ canonical radiosity solutions have been calculated, the radiosity solution for any sky luminance distribution for a specified time and date can be calculated as a weighted sum of the canonical radiosity solutions, where the weights are the luminances of the associated sky and solar patches as predicted by the chosen sky model. Such solutions may be calculated in milliseconds, as opposed to minutes to hours for prior art approaches.

The present invention employs an approach wherein the solar altitudinal and azimuthal angles are used to determine the altitudinal and azimuthal angles of the three closest solar patch centers. The radiosity solution for the direct sunlight is then calculated as the weighted sum of the three canonical radiosity solutions for these patches, with the weights being the luminous intensities of the direct sunlight at the respective altitudinal and azimuthal angles.

Virtual Photosensors

A disadvantage of the radiative flux transfer techniques is that while the radiosity solution may provide the luminance and approximate spectral radiant exitance at any selected point on a surface element, it is computationally expensive to measure the illuminance at an arbitrary position and orientation in the virtual environment, such as would be performed with a photometer in a physical environment. The calculations become prohibitively expensive when hundreds of meter positions must be calculated for climate-based annual daylight metrics such as spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), where the meters are typically positioned 0.6 meters above the floor.

In another novel contribution of the present invention, a virtual photosensor is modeled as a transparent surface element with a finite area. As previously described and in accordance with U.S. Pat. No. 4,928,250, each iteration of the radiative flux transfer process transferred unsent flux from a selected light source or surface element to all other elements visible to that light source or element. However, each iteration then continues by transferring the same unsent flux to all photosensor elements visible to that light source or element. The photosensor elements record the flux they have received, but in being transparent they do not absorb or reflect any of the flux. Once the radiative flux transfer process has converged to a solution, the photosensors have measured the incident spectral radiant flux and hence illuminance without participating in the radiative flux transfer process.

Transition Surfaces

A serious disadvantage of representing diffuse daylight as ‘n’ separate infinite-distance light sources that emits parallel beams of light is that the discrete nature of the beams becomes evident when projected through small windows and openings separating the exterior environment from the interior environment (FIG. 3). These parallel beams tend to produce visible “spokes” of light patterns on the interior surfaces. Worse, they may contribute to serious errors in the subsequent radiative flux transfer calculations for the interior surface elements.

A prior art solution as implemented for example by AGi32 is to capture a virtual hemispherical (“fisheye”) image of the exterior environment 300 as seen from the center of each window or opening 310 (referred to as a “transition” surface) or portion thereof. In a preferred embodiment, the method of capture utilizes a “hemicube” or similar geometric construct as disclosed in U.S. Pat. No. 4,928,250. This image is then projected from said center into the interior environment 320. Each pixel of the virtual image projects the average luminance or spectral radiant exitance of the exterior elements it represents onto the interior elements, thereby effecting radiative flux transfer. (A portion of the luminous flux or spectral radiant flux may be absorbed or reflected by a window surface.)

It is evident that each pixel of the virtual image represents a ray of luminous or spectral radiant flux emanating from the exterior environment that intersects the window or opening at the position of the virtual camera before transitioning into the interior environment. It is further evident that the angle of incidence q between said ray and the window surface normal can be precalculated. The reduction in transmitted flux (that is, the specular transmission coefficient) through a transparent window (glass, plastic, or more generally a dielectric material) due to Fresnel reflection can therefore be calculated on a per-pixel basis according to Schlick's approximation to the Fresnel equations:

T(θ)=1−R(θ)=(1−(R0+(1−R0)(1−cos(θ))**5)

where R0 is the surface reflectance at normal incidence (i.e., θ=0), the Fortran operator “**” in the equation meaning “to the power of”.

In cases where the dimensions of the window or opening are large with respect to the dimensions of the interior environment, it is advantageous to spatially divide the window or opening into a multiplicity of subareas called “patches,” with the virtual camera (hemicube) positioned at the center of each patch.

A disadvantage of this approach is that the images project only luminous or spectral radiant flux. In order to project the luminous or spectral radiant flux distribution of the exterior environment into the interior environment for each of the ‘n’ canonical radiosity solutions, it would be necessary to repeat the transfer process ‘n’ times for each transition surface. This process involves calculating the form factors of all of the exterior and interior environment elements visible from the transition surface, which as previously noted is computationally very expensive.

The present invention avoids this computational expense by first assigning a unique identifier to every finite element in the exterior and interior environments, then transferring the exterior environment element identifiers associated with each pixel rather than their average luminance or spectral radiant exitance. When these pixels are “projected” onto the interior environment elements, it is possible to access the ‘n’ luminance or spectral radiant exitance values assigned to each exterior environment element through their assigned identifiers. In this manner, the ‘n’ radiosity solutions for the exterior environment can be transferred to the interior environment without any significant computational burden.

The above approach assumes that the windows are fully transparent. Recent advances in daylight control for high performance buildings have however introduced glazing systems with complex bidirectional scattering distribution functions (BSDFs), including light redirecting materials such as prismatic glass and plastics, holographic films, and Fresnel lens arrays.

To address such systems, it is noted that such glazing systems both redirect and scatter the incident flux. Thus, rather than simply projecting a captured virtual image into the interior environment, each pixel can be assigned a scattering distribution function that describes how an incident ray with given altitudinal and azimuthal angles corresponding to the pixel will be scattered. These functions can be compactly represented using spherical and hemispherical harmonics, wavelets, radial basis functions, and other two-dimensional signal compression techniques as will be known to those skilled in the art of image compression. The BSDF function coefficients may be measured or calculated from virtual representations of the glazing systems.

Interior Environment

For interior environments, the light sources are the direct sunlight, diffuse daylight, and reflected light from exterior environment elements transferred through the transition surfaces, plus the artificial light emitted by the luminaires.

For daylight harvesting purposes, one or more luminaires are typically be assigned to “zones” that can be independently dimmed or switched. For the purposes of illustration, there are assumed to be ‘p’ luminaire zones in interior environment, where ‘p’ may be less than or equal to the number of luminaires.

As with the exterior environment, the present invention assigns a multiplicity of ‘n’+‘p’ unsent flux parameters and ‘n’+‘p’ luminous or spectral radiant exitance parameters to each interior environment element, where ‘n’ is the number of divisions of the sky dome and solar path. Radiative flux transfer techniques can again be employed to calculate the distribution of luminous or spectral radiant flux due to interreflections between surface elements in the interior environment for each sky and solar patch and each luminaire zone. (Similar to the sky dome and solar patches, each luminaire zone is assigned unit luminous or spectral radiant intensity.) The result is the generation of ‘n’+‘p’ separate canonical radiosity solutions for the interior environment.

In one embodiment, the set of radiosity solutions comprises a list of ‘n’ luminous exitance or spectral radiant exitance values for each exterior geometric element and ‘n’+‘p’ luminous exitance or spectral radiant exitance values for each interior geometric element. In another embodiment, each such list is compressed using for example wavelet, radial basis function, or similar lossless or lossy signal compression techniques as will familiar to those skilled in the art of data communications. Further compression may be achieved through correlations between the exitance lists of geometrically adjacent or close elements, as will be familiar to those skilled in the art of multichannel data compression for electroencephalograms, electrocardiograms, and the like.

It is further noted that the number of finite elements ‘m’ representing an interior environment, each with an array ‘n’+‘p’ of unsent flux and luminous exitance values, represents an ‘m’×(‘n’+‘p’) array. This array can be compressed using for example two-dimensional singular value decomposition (SVD) or eigenvector techniques such as are used for image compression, and an approximate representation of a portion of the matrix reconstructed as required with limited memory.

Optimal Luminous Environment

It is important to note that while the calculation of the ‘n’ canonical radiosity solutions for the exterior environment and ‘n’+‘p’ canonical radiosity solutions for the interior environment may require minutes to hours of computation time, these calculations need to be calculated only once for a given environment. Thereafter, the radiosity solution for any sky luminance distribution and any dimming or switched state for the luminaire zones can be calculated as a weighted sum of the canonical radiosity solutions, where the weights are the luminances of the associated sky dome and solar patches and the luminous intensities of the luminaires for each zone. These calculations can be performed in milliseconds.

A “sky condition” is uniquely determined according to the Perez (or similar) sky model by the time of day, the Julian date, the geographical position of the site in terms of longitude and latitude, and the direct normal irradiance and diffuse horizontal irradiance, which determines the spatial distribution of sky luminance and solar position. For this sky condition, there will be a range of luminaire zone dimmer or switch settings and automated fenestration states that provide a comfortable luminous environment for the occupants of the interior environment and minimize energy consumption. FIG. 8 shows for example a building 800 with a window 810 that admits daylight 830 from the Sun 820 and sky, wherein the amount of daylight is determined by the weather 840. Daylight sensor 850 senses the amount of daylight entering the building and artificial light 860 received from electric light source 870 and communicates the information to controller 880, which subsequently adjusts the amount of artificial light.

These are often competing goals. Occupants will be interested in having relatively constant illuminance of their workplaces, minimal glare from the windows, and infrequent changes in the automated fenestration devices (especially motorized blinds). Minimizing energy consumption may however dictate frequent changes in workplace illuminance and fenestration devices, especially on partly cloudy days where the available daylight may change rapidly.

Assuming however that these competing goals can be satisfactorily resolved, the building controller operation will be as shown in FIG. 4. In Step 400, the building controller 45 reads the input devices 10 to 40 shown in FIG. 1. In Step 405, the building controller 45 calculates an optimal luminous environment. This environment will be simulated by suitable weightings of the precalculated canonical radiosity solutions. This step may include thermal and air flow calculations in response to solar heat gain. In Step 410, the controller 45 sets the dimmers or switches the circuit of the luminaire zones (i.e., the electric lighting devices 55) and in accordance with the weights of the ‘p’ canonical radiosity solutions for the luminaire zones. Similarly, the settings of the automated fenestration devices are set in accordance with the average transmittance of the windows (i.e., the transition surfaces), and the HVAC devices are set in accordance with the thermal and air flow calculations.

Greenhouse lighting will not typically needed to be dimmed during the day, as its primary purpose is to provide supplemental lighting necessary for plant growth and health. It is however necessary for the greenhouse controller 145 to record the daylight measured by photosensors 100 at regular intervals and calculate the Daily Light Integral. If the value of this metric is less than that required for the specific plant, then supplemental lighting may be used to meet the plant's daily requirements. The greenhouse controller operation will otherwise be the same as shown in FIG. 4, although the thermal and air flow calculations will likely need to account for changes in humidity due to evaporation.

FIG. 5A shows the process of reading the input devices for building controller 45 in more detail, namely reading the daylight photosensors 10 (Step 500), reading the occupancy or vacancy sensors 15 (Step 505), reading the timers 20 (Step 510), reading the personal lighting controls 25 (Step 515), reading the utility smart power meters 30 (Step 520), reading the current weather report feed 35 (Step 525), and reading the temperature sensors 40 (Step 530) if present. Of course, the sequence shown in FIG. 5 is for illustration purposes only, and so is arbitrary.

FIG. 5B shows the process of reading the input devices in more detail for greenhouse controller 145, namely reading the daylight photosensors 100 (Step 535), reading the temperature sensors 105 (Step 540), reading the timers 110 (Step 545), reading the humidity sensors 115 (Step 550), reading the wind speed and direction sensors 120 (Step 555), reading the soil moisture sensors 125 (Step 560), reading the carbon dioxide and/or oxygen gas sensors 130 (Step 565), reading the utility smart power meter 135 (Step 570), and reading the current weather report feed 140 (Step 575). Of course, the sequence shown in FIG. 5B is for illustration purposes only, and so is arbitrary.

FIG. 6 shows the process of simulating the interior luminous environment (i.e., the distribution of luminous exitance over the interior environment elements). In Step 600, the current time and date is determined. This uniquely determines the solar altitudinal and azimuthal angles. In Step 605, the current sky condition is determined from the direct normal irradiance and diffuse horizontal irradiance, which may be obtained for example from the weather report feed 35 in FIG. 1A of 140 in FIG. 1B. It may also however be inferred from the daylight photosensor readings, as will be disclosed below.

The luminaire zone, automated fenestration, and environmental device settings are determined in Step 610. These settings will in general be constrained by the input devices data, including the daylight photosensors 10, occupancy sensors 15, timers 20, personal lighting controls 25, utility power meters 30, weather report feeds 35, and temperature sensors 40 shown in FIG. 1A, and the daylight photosensors 100, temperature sensors 105, timers 110, humidity sensors 115, wind sensors 120, soil moisture sensors 125, gas sensors 130, utility power meters 135, and weather report feeds 140 shown in FIG. 1B.

Further constraints may of course be imposed or implied by the requirements of external systems accessed through the communication ports 55 or 155, such as for example an HVAC system controller, an energy storage system controller, a building automation system, a smart power grid, and so forth.

The interior luminous environment and optionally thermal environment is calculated in Step 620 using a weighted summation of the precalculated canonical radiosity solutions as previously disclosed.

The interior luminous and thermal environments are examined in Step 625 to determine whether the goals of comfortable luminous and thermal environments for the occupants, or minimum requirements for plant growth and health, and the minimization of energy consumption have been attained. If not, Steps 610 through 625 are repeated.

FIG. 7A shows the output process of the controller 45, which consists of setting the luminaire zone dimmers and switches in Step 700, setting the automated fenestration device states in Step 705, and setting the HVAC device states in Step 710. Not shown is communication with external systems with which the controller 45 may exchange information and commands.

FIG. 7B shows the output process of the greenhouse controller 145, which consists of setting the supplemental lighting dimmers and switches in Step 715, setting the automated fenestration device states in Step 720, setting the humidity control device states in Step 725, setting the CO2 distribution device states in Step 730, and setting the HVAC device states in Step 735. Not shown is communication with external systems with which greenhouse controller 145 may exchange information and commands.

Optimization

Referring to FIG. 1A, the controller 45 comprises a neuro-fuzzy or similar logic inference engine that can be trained using for example deep learning techniques to generate optimal luminaire zone dimmer and switch settings, automated fenestration device states, and optimal HVAC device settings for any given sky condition or time sequence thereof to satisfy the goals of comfortable luminous and thermal environments for the occupants and the minimization of annual energy consumption.

Referring to FIG. 1B, the greenhouse controller 145 similarly comprises a neuro-fuzzy or similar logic inference engine that can be trained using for example deep learning techniques to generate optimal luminaire zone dimmer and switch settings, automated fenestration device states, and optimal HVAC and other environmental settings for any given sky condition or time sequence thereof to satisfy the goals of minimal requirements for plant growth and health, and the minimization of annual energy consumption.

A key requirement of any logic inference engine is an input data set from which the engine can be trained to generate suitable outputs. Given the ability to quickly simulate the interior luminous environment using the radiosity-based method disclosed herein, suitable training data sets may include the Typical Meteorological Year (TMY) historical weather data files available for most geographic locations in North America (or equivalent weather data for other continents). These files provide hour-by-hour direct solar irradiance and diffuse irradiance values that can be used directly with the present invention.

As will be appreciated by those skilled in the art, there are many different artificial intelligence engines that can be trained to generate suitable output values for a range of input values; the neuro-fuzzy logic engine is merely one embodiment.

With the ability to train the building controller 45 or greenhouse controller 145 offline prior to deployment using a virtual representation of the environment, it becomes possible to design a daylight harvesting system with predictable energy savings performance. This includes the ability to locate and orient the daylight photosensors within the virtual interior environment such that the neuro-fuzzy logic engine can learn to correlate their outputs with known virtual sky conditions. When the controller is used with physical daylight photosensors within the physical interior environment, it is likely that the controller will be able to infer the correct sky condition strictly from the photosensor outputs without the need for direct normal and diffuse horizontal irradiance values from the weather report feed.

In one embodiment, the desired spatial illuminance distribution due to the combination of daylight and electric lighting across surfaces and workplanes in the virtual representation of the environment for each time and Julian date under various sky conditions is densely sampled with virtual photosensors spaced for example 30 centimeters on center. The predicted virtual photosensor outputs are arranged in a rectangular array. Using singular value decomposition (SVD) or eigenanalysis, the dominant singular vectors or eigenvectors of the array are determined. These vectors typically require much less storage space than the full matrix, and can later be used to reconstruct an approximation of the full matrix using for example truncated SVD for determining correlations between the virtual photosensor outputs and the spatial illuminance distributions.

In another embodiment, the virtual photosensor outputs are correlated directly with the dominant singular vectors or eigenvectors of the arrays, thereby eliminating the need to reconstruct approximations of the full matrices.

With multiple daylight photosensors 10 or 100, possibly mounted in each luminaire, it further becomes possible for the controller to perform data fusion by dynamically combining the photosensor outputs. That is, the controller may autonomously learn during its training with the TMY data set that a given weighting of photosensor outputs reliably predicts the correct sky condition during the morning, while another weighting is better during the afternoon. These weighted photosensor outputs can similarly be correlated with the corresponding spatial illuminance distributions or their corresponding dominant singular vectors or eigenvectors during the training period.

With multiple gas concentration sensors 130, it further becomes possible for the controller to perform data fusion by dynamically combining the gas sensor outputs. That is, the controller may autonomously learn during its training with the TMY data set that a given weighting of gas sensor outputs reliably predicts the desired gas concentration for a given sky condition during the morning, while another weighting is better during the afternoon. These weighted gas sensor outputs can similarly be correlated with the corresponding spatial illuminance distributions or their corresponding dominant singular vectors or eigenvectors during the training period.

Once the building controller 45 or greenhouse controller 145 has been suitably trained, the three-dimensional finite element representation of the exterior and interior environments may optionally be removed from the controller memory, as it may be sufficient during operation of the controller to determine the most likely spatial illuminance distribution according to its correlation with the physical photosensor outputs. This eliminates the need to recalculate the spatial illuminance distribution in real time according to the photosensor outputs.

If on the other hand the three-dimensional finite element representation of the exterior and interior environments is retained in the controller memory or otherwise readily accessible to the controller, the radiative flux transfer can be iteratively recalculated with changes to the material reflectances, window transmittances, lamp lumen outputs, and other model parameters at regular intervals (for example, once per week) to improve the correlations between the predicted and measured photosensor outputs, or to suggest more appropriate positions and orientations for the physical photosensors.

Similarly, the controller may autonomously learn during its training with the TMY data set that the goals of a comfortable luminous and thermal environment for the occupants and the minimization of annual energy consumption are best satisfied with a dynamic configuration of luminaire zones. This may be particularly easy to accomplish if each luminaire is assigned an Internet address or other unique identifier such that a single dimming command can be sent to any number of luminaires.

Further, controller training need not be limited to the TMY or equivalent weather data set. As will be known to those skilled in the art, the controller can learn to adapt its behavior to changes in the environment, such as changing user preferences for illumination or occupancy sensor events, or to changes in the requirements of external systems such as for example an HVAC controller or smart power grid.

In another embodiment, the three-dimensional finite element representation of the interior environment is augmented with virtual representation of occupants (for example, office workers) whose stochastic behavior includes movement within the environment to simulate occupancy sensor events, and whose user preferences dim or switch the luminaires at various locations (such as for example open office cubicles or workstations). These virtual occupants can be used to similarly train the controller offline prior to deployment, and to determine an optimal placement of occupancy sensors.

Following deployment, the controller can similarly record occupancy sensor events and personal lighting control operations to refine the stochastic behavior of the virtual occupants.

During the controller training period, problems such as lamp failures, lamp lumen depreciation, photosensor and occupancy sensor failures, and network communication failures can be simulated, thereby enabling the controller to learn appropriate response strategies to minimize energy consumption, optimize spatial illuminance distributions, and satisfy other predetermined goals.

It will further be evident to those skilled in the art that neuro-fuzzy logic and similar artificial intelligence engines can be trained to recognize temporal patterns. In particular, the controller can implement a Kalman filter or other construct to observe a minute-by-minute sequence of sky conditions and predict the sky condition and occupant behavior (as determined for example by the occupancy sensor outputs and personal lighting control commands) for some period of time into the future. Based on this ongoing sequence of predictions, it can develop a long-term strategy of daylight harvesting system settings that will satisfy the goals of occupant comfort and annual energy savings.

The controller may also receive input data from and send output data to a plurality of buildings that may be geographically proximate or remote. The controller may thereby learn commonalities and differences in operational requirements between a possibly disparate set of buildings that further refines its behavior to changes in the environment.

Finally, in yet another embodiment, the physical controller is simulated in software for the purposes of training during the design phase. The resultant data from the training period is then used to program the physical controller prior to deployment.

Controller Topology

In one embodiment, the building controller 45 or greenhouse controller 145 is implemented as a centralized system. In another embodiment, the controller is implemented as a decentralized and self-organizing system with computational intelligence embedded in each luminaire and associated “Internet of Things” hardware devices connected via a bidirectional communications network.

The luminaire network topology may be based on swarm intelligence, such as for example particle swarm or ant colony optimization, where the goals are for example desired spatial illuminance distributions, minimal energy consumption, user preferences, minimal visual glare, and so forth.

Reinforcement learning algorithms can be employed to provide self-learning capabilities for the controller in achieving these goals. In one embodiment, the controller can simultaneously operate the physical daylight harvesting system in real time and model the environment offline using “what-if” scenarios to improve controller performance over time, wherein these scenarios may be implemented using evolutionary algorithms, including genetic algorithms.

FIG. 9 shows a plurality of sensors and data feeds 910 providing real-time information to a controller 920. In Step 930, the controller reads the input data, then in Step 940 performs a virtual simulation of the building environment based on the virtual simulation parameters 980. The results of this simulation are compared with the predicted sensor data in Step 950. If the difference between the predicted and measured data exceeds predefined limits, the simulation parameters are updated and Steps 940 and 950 are repeated; otherwise Step 970 outputs commands to the external devices 990.

Aquaculture

In another embodiment, the environment is an aquaculture system comprised of tanks, ponds, or ocean and lake enclosures used to raise fish for commercial purposes, where overhead or underwater lighting may be used to control feeding, reproduction, circadian rhythms, and other species management factors (US 2014/0355254). Supplemental lighting may be required, particularly during winter months. Given the often remote locations of marine net pens, providing electrical power often entails considerable costs.

In addition to the Perez sky model or similar mathematical model for predicting sky conditions, a mathematical model representing the distribution of light in turbid fluid media, such as for example Perez-Cazorla et al. 1997, “Global Illumination Techniques for the Simulation of Participating Media,” Proc. Eighth Eurographics Workshop on Rendering, may be employed. Such models enable the prediction of illuminance and other photometric parameters at specified depths and distances for specified or measured water turbidity. Additional parameters such as tidal depth and fish or shellfish behavior models may also be employed.

Aspects of the present disclosure may be implemented as a method performed by a predictive daylight harvesting system. FIG. 10 is a flow diagram depicting an example method 1000.

At 1010, the method includes obtaining one or more input values identifying a target distribution of direct and interreflected radiation within an environment. Additionally or alternatively at 1010, the method includes obtaining one or more input values identifying a geographic location, a time of day, and a date. Input values may be obtained received from external sources, internally generated, or retrieved from memory. As an example, a target distribution of radiation may be user-defined, whereas the time and date may refer to the current time and date, or a future time and date. The geographic location may refer to a geographic location of a building or other environment that is to be controlled.

At 1012, the method includes calculating an expected sky condition for the geographic location, the time of day, and the date based on a mathematical model. In an example, the mathematical model incorporates historical weather data. Alternatively or additionally, the mathematical model was previously trained on or using historical weather data. Alternatively or additionally, the method may include obtaining a current weather feed from a remote device over a communications network, and calculating the expected sky condition may be further based on information contained in the current weather feed.

At 1014, the method includes calculating a predicted distribution of direct and interreflected solar radiation within the environment based on the expected sky condition. In at least some implementations, calculating the predicted distribution of direct and interreflected solar radiation within the environment based on the expected sky condition includes determining a transfer of solar radiation through one or more windows or openings within a virtual representation of the environment by: (1) assigning a unique identifier to each surface element in the virtual representation of the environment; (2) capturing an image of visible surface elements of an exterior of the environment as seen through a window or an opening of the one or more windows or openings; (3) projecting the image onto the visible surface elements of an interior of the environment as seen through the window or the opening; and (4) indirectly determining luminance or spectral radiant exitance transferred from an exterior surface element to a corresponding interior surface element for one of a multiplicity of canonical radiosity solutions by way of the unique identifier of the exterior surface element.

At 1016, the method includes obtaining measurement data from one or more photosensors measuring an initial distribution of direct and interreflected radiation within the environment. The initial distribution of direct and interreflected radiation may include radiation from solar and/or electrical lighting sources. In at least some implementations, the measurement data obtained from the one or more photosensors provides a less dense spatial sampling of the initial distribution of direct and interreflected radiation within the environment than the predicted distribution of direct and interreflected solar radiation within the environment.

At 1018, the method includes determining, based on the measurement data and the predicted distribution of direct and interreflected solar radiation, a target distribution of direct and interreflected artificial electromagnetic radiation produced by electrical lighting to achieve the target distribution of direct and interreflected radiation within the environment. In at least some implementations, the target distribution of direct and interreflected artificial electromagnetic radiation produced by the electrical lighting may be calculated by performing radiative flux transfer calculations with a virtual representation of the environment. The virtual representation may include geometry and material properties of objects that influence the distribution of luminous flux in the environment. As an example, a photosensor in the virtual representation of the environment may comprise a transparent surface element with a finite area that records radiant flux transferred from a selected light source or surface element at each iteration of the radiative flux transfer calculations.

At 1020, the method includes setting output parameters to one or more devices controlling admittance of solar radiation into the environment and/or controlling the electrical lighting to modify the initial distribution to achieve the target distribution of direct and interreflected radiation within the environment. The target distribution of direct and interreflected radiation within the environment may include solar and/or artificial electromagnetic radiation components.

In at least some implementations, the method at 1020 may further include setting the output parameters to maximize the solar radiation admitted into the environment as a component of the target distribution of direct and interreflected radiation while maintaining one or more additional environmental parameters within a predefined range. As an example, the output parameters may be set to supplement the solar radiation admitted into the environment with the target distribution of direct and interreflected artificial electromagnetic radiation produced by the electrical lighting. Non-limiting examples of environment parameters include temperature, humidity, light spectrum, light intensity, and/or other parameters described herein.

In at least some implementations, the method may further include calculating a distribution of solar heat gain within the environment based on the measurement data and/or the predicted distribution of direct and interreflected solar radiation. One or more effects of the distribution of solar heat gain on one or more environmental parameters may be calculated. A target value for each environmental parameter may be obtained. Setting the output parameters to one or more devices may further varying a relative proportion of solar radiation to artificial electromagnetic radiation within the target distribution of direct and interreflected radiation to achieve the target value for each of the one or more environmental parameters. Additional measurement data may be received from one or more additional sensors measuring the one or more environmental parameters within the environment. Calculating the one or more effects of the distribution of solar heat gain on the one or more environmental parameters may be further based on the additional measurement data received from the one or more additional sensors.

In at least some implementations, the method may further include receiving one or more information feeds from external sources. The measurement data, the information feeds, and the output parameters may be used to train an artificial intelligence engine to control the one or more devices in response to the measurement data and information feeds.

In at least some implementations, the methods and processes, or portions thereof described herein may be tied to a computing system that includes one or more computing devices. Such methods and processes may be implemented as a computer-application program or service, an application-programming interface (API), a library, and/or other computer-program type.

FIG. 11 is a schematic diagram depicting an example computing system 1100. Computing system 1100 is a non-limiting example of a control system or controller that forms part of a predictive daylight harvesting system. Computing system 1100 can perform the methods and processes, or portions thereof described herein. Computing system 1100 is shown in simplified form. Computing system 1100 may take the form of one or more personal computers, server computers, mobile computing devices, electronic controller devices, and/or other computing devices.

Computing system 1100 includes a logic subsystem 1110 and a storage subsystem 1112. Computing system 1100 may further include an input subsystem 1114, an output subsystem 1116, a communication subsystem 1118, and/or other components not shown in FIG. 11.

Logic subsystem 1110 may include one or more physical logic devices configured to execute instructions. For example, the logic subsystem may be configured to execute instructions that are part of one or more applications, services, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more components, achieve a technical effect, or otherwise arrive at a desired result.

The logic subsystem may include one or more processors (as an example of physical logic devices) configured to execute software instructions. Additionally or alternatively, the logic subsystem may include one or more hardware and/or firmware logic machines (as an example of physical logic devices) configured to execute hardware or firmware instructions. Processors of the logic subsystem may be single-core or multi-core, and the instructions executed thereon may be configured for sequential, parallel, and/or distributed processing. Individual components of the logic subsystem may be distributed among two or more separate devices, which may be remotely located and/or configured for coordinated processing. Aspects of the logic subsystem may be virtualized and executed by remotely accessible, networked computing devices configured in a cloud-computing configuration.

Storage subsystem 1112 includes one or more physical, non-transitory memory devices configured to hold instructions executable by the logic subsystem in non-transitory form, to implement the methods and processes described herein. When such methods and processes are implemented, the state of storage subsystem 1112 may be transformed—e.g., to hold different data. Storage subsystem 1112 may include removable and/or built-in devices. Storage subsystem 1112 may include optical memory devices, semiconductor memory devices, and/or magnetic memory devices, among other suitable forms. Storage subsystem 1112 may include volatile, nonvolatile, dynamic, static, read/write, read-only, random-access, sequential-access, location-addressable, file-addressable, and/or content-addressable devices. Aspects of logic subsystem 1110 and storage subsystem 1112 may be integrated together into one or more hardware-logic components. While storage subsystem 1112 includes one or more physical devices, aspects of the instructions described herein alternatively may be propagated by a communication medium (e.g., an electromagnetic signal, an optical signal, etc.) that is not necessarily held by a physical device for a finite duration.

The terms “module,” “program,” and “engine” may be used to describe an aspect of computing system 1100 implemented to perform a particular function. In some cases, a module, program, or engine may be instantiated via logic subsystem 1110 executing instructions held by storage subsystem 1112. It will be understood that different modules, programs, and/or engines may be instantiated from the same application, service, code block, object, library, routine, API, function, etc. Likewise, the same module, program, and/or engine may be instantiated by different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms “module,” “program,” and “engine” may encompass individual or groups of executable files, data files, libraries, drivers, scripts, database records, etc. A “service”, as used herein, may refer to an application program executable across multiple user sessions. A service may be available to one or more system components, programs, and/or other services. In some implementations, a service may run on one or more server-computing devices.

Input subsystem 1114 may include or interface with one or more user-input devices such as a keyboard, mouse, touch screen, microphone etc. Input subsystem 1114 may include or interface with one or more sensor devices, such as previously described herein. Non-limiting examples of such sensor devices include daylight illuminance sensors, photosynthetic photon flux density sensors, ultraviolet radiation sensors, infrared radiation sensors, spectroradiometric sensors, temperature sensors, humidity sensors, soil moisture sensors, carbon dioxide sensors, oxygen sensors, wind speed/direction sensors, water turbidity sensors, gas concentration sensors, and plant phytometric sensors. Input subsystem 1114 may additionally receive input data from information feeds such as weather report feeds, building automation systems, energy storage systems, solar panels, electrical power utilities, and smart power grids.

Output subsystem 1116 may include or interface with one or more user-output devices such as a graphical display device, touch screen, audio speakers, etc. Output subsystem 1116 may include or otherwise interface with one or more devices that control admittance of solar radiation into an environment and/or control electrical lighting, as well as the various other control devices described herein. Other example devices that may form part of the output subsystem or may otherwise interface with the output system include luminaire switches and dimmers, moveable shades, electrochromic windows, and heating, ventilation, and air conditioning equipment. Such devices may be referred to as external devices if they are not integrated with an electronic controller.

Communication subsystem 1118 may be configured to communicatively couple computing system 1100 with one or more other devices. Communication subsystem 1100 may include wired and/or wireless communication devices compatible with one or more different communication protocols. As non-limiting examples, the communication subsystem may be configured for communication via a wired or wireless wide area network, local area network, and/or personal area network. In an example, the communication subsystem may allow computing system 1100 to send and/or receive messages to and/or from other devices via a communications network.

As described herein, a variety of information in the form of data may be measured, collected, received, stored, retrieved from storage, processed, analyzed, organized, copied, reported, and/or transmitted in raw and/or processed forms. Data includes a set of one or more values (i.e., data values) of one or more parameters or variables. Such values may be quantitate or qualitative in nature. Data may be represented by one or more physical quantities, attributes, or characteristics of one or more signals or object states.

An object state refers to a physical state of a tangible, physical object, such as a device or machine. Within the context of a computing system or other electronic system, an object state may include a value of a bit stored in a memory cell or other suitable bistable/multistable electronic circuit (e.g., flip-flop or latch) of a memory device. As an example, a value of a bit may be defined by a high or low physical voltage value of a memory cell, corresponding to values of 1 or 0 for the bit, respectively.

Data represented by one or more signals (i.e., data signals) may be propagated by a communication medium, in the form of electrical signals, electromagnetic signals, optical signals, etc. Data signals may be communicated over one or more wired and/or wireless communications links or paths. Data signals may take the form of or form part of a modulated signal or a non-modulated signal. Data signals may be formatted or otherwise organized into one or more messages, streams, packets, datagrams, and/or frames as defined by one or more communications protocols.

Data may be represented in a variety of digital and/or analog forms. Within the context of digital data, an object state or signal component representing an individual data unit may be observed or identified as having a discrete value of two or more discrete values. Within the context of analog data, an object state or signal component representing an individual data unit may be observed or identified as having a value within a range of non-quantized values.

A collection of data may take the form of a set instructions that are executable by a machine to perform one or more operations. Such instructions may be referred to as machine-readable instructions that direct the machine to perform one or more operations. A set of instructions may take the form of software or a portion thereof (e.g., a software component). Software may include firmware, an operating system, an application program or other program type, a software plug-in, a software update, a software module, a software routine, or other software component.

An organized collection of data may take the form of a database system or other suitable data structure (e.g., an electronic file). A database system includes one or more databases that define relationships and associations between and among data objects. As an example, a data object (e.g., a user identifier) that includes a set of one or more data values may be associated with one or more other data objects (e.g., a user setting). A database system may be integrated with or form part of a software component.

Data may include metadata that describes other data. Metadata describing the structure of other data, such as a relationship or association of data objects in a database may be referred to as structural metadata. Metadata describing the content of other data may be referred to as guide metadata. A collection of data may include metadata and other data described by that metadata.

The configurations and/or approaches described herein are exemplary in nature, and specific implementations or examples are not to be considered in a limiting sense, because numerous variations are possible. The specific methods or processes described herein may represent one or more of any number of processing strategies. As such, various acts illustrated may be performed in the sequence illustrated, in other sequences, in parallel, or in some cases omitted. Likewise, the order of the above-described processes may be changed. The subject matter of the present disclosure includes all novel and nonobvious combinations and subcombinations of the various methods, processes, systems and configurations, and other features, functions, acts, and/or properties disclosed herein, as well as any and all equivalents thereof.

The embodiments of the invention may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the claims. 

1. A method performed by a predictive system for indoor horticulture, the method comprising: obtaining a set of input values identifying a geographic position of a physical structure enclosing an interior environment and a target distribution for each of a set of environmental parameters for a selected plant occupant, the set of environmental parameters including at least a light level, temperature level, and humidity level within the interior environment; obtaining a virtual representation of the physical structure including geometry and material properties of physical objects defining an enclosure of the physical structure and located within the interior environment that influence an actual distribution of the set of environmental parameters within the interior environment, and further including virtual representations of a set of one or more sensor devices and a set of one or more control devices as physically arranged with respect to the physical structure and interior environment; iteratively over time, updating the virtual representation based on actual plant growth or a predicted plant growth model for the selected plant occupant to incorporate changing virtual representations of the selected plant occupant that influence the actual distribution of the set of environmental parameters within the interior environment; running a computational model to obtain a predicted distribution of the set of environmental parameters for the virtual representation based on the set of input values; determining a target distribution of a set of artificially modulated environmental parameters including at least an artificially modulated light level, temperature level, and humidity level by way of the set of control devices to achieve the target distribution of the set of environmental parameters within the interior environment based on the predicted distribution of the set of environment parameters; obtaining measurement data from the set of sensor devices measuring an initial distribution of the set of environmental parameters, the set of sensor devices including one or more photosensors, one or more temperature sensors, and one or more humidity sensors; and based on the target distribution of the set of artificially modulated environmental parameters, setting one or more output parameters for the set of control devices to collectively control the actual distribution of the set of environmental parameters towards achieving the target distribution of the set of environmental parameters, the set of control devices selected from a group comprising: luminaire devices, fenestration devices, HVAC devices, humidity modulation devices.
 2. The method of claim 1, further comprising: obtaining a set of additional inputs identifying a change to the virtual representation with respect to the physical objects, the set of sensor devices, or the set of control devices; and updating the virtual representation based on the set of additional inputs to incorporate the change to the virtual representation.
 3. The method of claim 1, wherein setting one or more output parameters for the set of control devices includes a controller of the predictive system implementing an artificial intelligence engine that identifies control values for the one or more output parameters further based on a set of additional inputs obtained, including one or more of: weather data, occupant usage data, plant growth data, alternative energy availability data, and/or measurement data from additional sensor devices, including one or more of soil moisture sensors, carbon dioxide and oxygen concentration sensors, and/or sensors directly monitoring plant growth and health.
 4. The method of claim 3, wherein the artificial intelligence engine applies fuzzy logic to the set of additional inputs to identify the control values for the one or more output parameters.
 5. The method of claim 6, further comprising: iteratively over time, training the controller of the predictive system based on the measurement data and the one or more output parameters for the set of control devices to establish a trained relationship between the measurement data and the one or more output parameters; and upon establishing the trained relationship, setting the one or more output parameters for the set of control devices based on the trained relationship in place of or in addition to the target distribution of the set of artificially modulated environmental parameters.
 6. The method of claim 3, wherein the artificial intelligence engine applies neuro-fuzzy logic to the set of additional inputs to identify the control values for the one or more output parameters.
 7. The method of claim 3, wherein the artificial intelligence engine applies deep learning methods to the set of additional inputs to identify the control values for the one or more output parameters.
 8. The method of claim 7, further comprising: iteratively over time, training the controller of the predictive system based on the measurement data and the one or more output parameters for the set of control devices to establish a trained relationship between the measurement data and the one or more output parameters; and upon establishing the trained relationship, setting the one or more output parameters for the set of control devices based on the trained relationship in place of or in addition to the target distribution of the set of artificially modulated environmental parameters.
 9. A predictive system for indoor horticulture, the system comprising: a computing system, including: a logic subsystem; and a storage subsystem having instructions stored thereon executable by the logic subsystem to: obtain a set of input values identifying a geographic position of a physical structure enclosing an interior environment and a target distribution for each of a set of environmental parameters for a selected plant occupant, the set of environmental parameters including at least a light level, temperature level, and humidity level within the interior environment; obtain a virtual representation of the physical structure including geometry and material properties of physical objects defining an enclosure of the physical structure and located within the interior environment that influence an actual distribution of the set of environmental parameters within the interior environment, and further including virtual representations of a set of one or more sensor devices and a set of one or more control devices as physically arranged with respect to the physical structure and interior environment; iteratively over time, update the virtual representation based on actual plant growth or a predicted plant growth model for the selected plant occupant to incorporate changing virtual representations of the selected plant occupant that influence the actual distribution of the set of environmental parameters within the interior environment; run a computational model to obtain a predicted distribution of the set of environmental parameters for the virtual representation based on the set of input values; determine a target distribution of a set of artificially modulated environmental parameters including at least an artificially modulated light level, temperature level, and humidity level by way of the set of control devices to achieve the target distribution of the set of environmental parameters within the interior environment based on the predicted distribution of the set of environment parameters; obtain measurement data from the set of sensor devices measuring an initial distribution of the set of environmental parameters, the set of sensor devices including one or more photosensors, one or more temperature sensors, and one or more humidity sensors; and based on the target distribution of the set of artificially modulated environmental parameters, set one or more output parameters for the set of control devices to collectively control the actual distribution of the set of environmental parameters towards achieving the target distribution of the set of environmental parameters, the set of control devices selected from a group comprising: luminaire devices, fenestration devices, HVAC devices, humidity modulation devices.
 10. The system of claim 9, wherein the instructions are further executable by the logic subsystem to: obtain a set of additional inputs identifying a change to the virtual representation with respect to the physical objects, the set of sensor devices, or the set of control devices; and update the virtual representation based on the set of additional inputs to incorporate the change to the virtual representation.
 11. The system of claim 9, wherein setting one or more output parameters for the set of control devices includes implementing an artificial intelligence engine of the instructions that identifies control values for the one or more output parameters further based on a set of additional inputs obtained, including one or more of: weather data, occupant usage data, plant growth data, alternative energy availability data, and/or measurement data from additional sensor devices, including one or more of soil moisture sensors, carbon dioxide and oxygen concentration sensors, and/or sensors directly monitoring plant growth and health.
 12. The system of claim 11, wherein the instructions are further executable by the logic subsystem to: iteratively over time, train the artificial intelligence engine based on the measurement data and the one or more output parameters for the set of control devices to establish a trained relationship between the measurement data and the one or more output parameters; and upon establishing the trained relationship, set the one or more output parameters for the set of control devices based on the trained relationship in place of or in addition to the target distribution of the set of artificially modulated environmental parameters.
 13. The system of claim 12, wherein the artificial intelligence engine applies fuzzy logic to the set of additional inputs to identify the control values for the one or more output parameters.
 14. The system of claim 12, wherein the artificial intelligence engine applies neuro-fuzzy logic to the set of additional inputs to identify the control values for the one or more output parameters.
 15. The system of claim 12, wherein the artificial intelligence engine applies deep learning methods to the set of additional inputs to identify the control values for the one or more output parameters.
 16. The system of claim 9, further comprising: the set of sensor devices.
 17. The system of claim 9, further comprising: the set of control devices. 